Papers Personalized Federated Learning
“Personalized Federated Learning” 태그가 달린 논문 378편 · 필터 해제
Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation
Statistical heterogeneity limits federated learning when a single global classifier cannot represent client-specific label distributions. In this work, we propose Personalized Federated Knowledge Distillation with Head A…
Personalized Federated LearningKnowledge DistillationCommunication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching
Personalized federated learning (PFL) is a promising paradigm for collaborative learning over distributed devices, where edge nodes collaboratively train personalized models without sharing raw data. Although PFL address…
Personalized Federated LearningFlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space
Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Exi…
Personalized Federated LearningGraph LearningDual Attention Heads for Personalized Federated Learning in ECG Classification
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterogeneity of electrocardiogram (ECG) data across healthcare providers pre…
Personalized Federated LearningECG ClassificationQFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems genera…
Personalized Federated LearningActivity RecognitionTowards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and participate in model updates under heterogeneous value constraints. The key …
Personalized Federated LearningTowards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design
The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device machine learning applications. While federated learning (FL) mitigates…
Personalized Federated LearningpFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning
Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR). However, most existing FU…
Personalized Federated LearningTowards Personalized Federated Learning for Dysarthric Speech Recognition
Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forc…
Personalized Federated LearningSpeech RecognitionFedSPC: Shared Parameter Correction for Personalized Federated Learning
Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into sha…
Personalized Federated LearningPattern Recognition Tasks with Personalized Federated Learning
Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing personalized model updates whilst upholding stringent data privacy pr…
Personalized Federated LearningSeparate Aggregation of Split Network for Personalized Federated Learning
Federated learning enables collaborative model training without sharing raw data, but its performance can degrade substantially under heterogeneous client data distributions. A single global model often cannot satisfy di…
Personalized Federated LearningPersonalized Federated Learning by Energy-Efficient UAV Communications
Federated learning (FL) is an effective paradigm for enhancing the learning capability of edge devices while preserving data privacy. In geographically dispersed FL systems, such as sensor networks in remote areas, unman…
Personalized Federated LearningCOSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution. While recent approaches attempt to address this challenge through c…
Personalized Federated LearningKnowledge DistillationFedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference
Federated Learning (FL) facilitates collaborative model training across decentralized clients while preserving data privacy by avoiding raw data exchange. Despite its potential, FL performance is often compromised by dat…
Personalized Federated LearningPersonalized Federated Learning for Gradient Alignment
Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized information. Local training is hindered by high variance gradients in…
Personalized Federated LearningFine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world scenarios often face the co-occurrence o…
Personalized Federated LearningTaming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but directly sharing them poses privacy risks. A common defense involves per…
Personalized Federated LearningDomain AdaptationHeterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditi…
Personalized Federated LearningFB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches typically rely on iterative optimizatio…
Personalized Federated Learning